A comparison of seven random‐effects models for meta‐analyses that estimate the summary odds ratio

Comparative trials that report binary outcome data are commonly pooled in systematic reviews and meta‐analyses. This type of data can be presented as a series of 2‐by‐2 tables. The pooled odds ratio is often presented as the outcome of primary interest in the resulting meta‐analysis. We examine the use of 7 models for random‐effects meta‐analyses that have been proposed for this purpose. The first of these models is the conventional one that uses normal within‐study approximations and a 2‐stage approach. The other models are generalised linear mixed models that perform the analysis in 1 stage and have the potential to provide more accurate inference. We explore the implications of using these 7 models in the context of a Cochrane Review, and we also perform a simulation study. We conclude that generalised linear mixed models can result in better statistical inference than the conventional 2‐stage approach but also that this type of model presents issues and difficulties. These challenges include more demanding numerical methods and determining the best way to model study specific baseline risks. One possible approach for analysts is to specify a primary model prior to performing the systematic review but also to present the results using other models in a sensitivity analysis. Only one of the models that we investigate is found to perform poorly so that any of the other models could be considered for either the primary or the sensitivity analysis.     

年轻人与老年人简单乘法运算之功能磁共振成像研究

目的应用fMRI技术探讨中国青年和老年人群在简单运算任务下脑激活模式及其与行为学之间的关系。方法分别对青年组(19例)和老年组(20例)健康志愿者进行对照任务和简单运算任务下的fMRI检查。结果两组受试者受教育程度(P=0.125)、智力水平(P=0.921),以及完成对照任务(P=0.142)和简单乘法运算任务(P=0.880)之正确率差异无统计学意义,但老年组受试者完成对照任务(P=0.000)和简单乘法运算任务(P=0.005)反应时间明显延长。青年组受试者在任务刺激下可激活右侧缘上回并向顶内沟和颞中上回后部延伸,中央前回和运动前区、前额叶,左侧缘上回并向颞上回后部和角回延伸,顶内沟区域、颞中下回,内侧后扣带回、楔前叶、辅助运动区、海马沟、海马旁回及前额叶内侧;老年组受试者则分别激活右侧缘上回和顶下区域并向颞中上回后部延伸,中央前回和运动前区、前额叶,左侧缘上回和角回并向顶下延伸,中央前回和运动前区、岛叶及前额叶,内侧后扣带回和中央旁小叶、前扣带回及前额叶内侧;两组受试者共激活脑区包括顶下区域、楔前叶、中央前后回和额顶叶网络,以及颞叶、海马旁回、钩回、屏状核和后扣带回等皮质下结构。结论数学事实提取相关网络的主要成分受年龄影响较小,老年人群的任务激活脑区主要向任务相关顶区集中。     

A neuromorphic network for generic multivariate data classification

Computational neuroscience has uncovered a number of computational principles used by nervous systems. At the same time, neuromorphic hardware has matured to a state where fast silicon implementations of complex neural networks have become feasible. En route to future technical applications of neuromorphic computing the current challenge lies in the identification and implementation of functional brain algorithms. Taking inspiration from the olfactory system of insects, we constructed a spiking neural network for the classification of multivariate data, a common problem in signal and data analysis. In this model, real-valued multivariate data are converted into spike trains using “virtual receptors” (VRs). Their output is processed by lateral inhibition and drives a winner-take-all circuit that supports supervised learning. VRs are conveniently implemented in software, whereas the lateral inhibition and classification stages run on accelerated neuromorphic hardware. When trained and tested on real-world datasets, we find that the classification performance is on par with a naïve Bayes classifier. An analysis of the network dynamics shows that stable decisions in output neuron populations are reached within less than 100 ms of biological time, matching the time-to-decision reported for the insect nervous system. Through leveraging a population code, the network tolerates the variability of neuronal transfer functions and trial-to-trial variation that is inevitably present on the hardware system. Our work provides a proof of principle for the successful implementation of a functional spiking neural network on a configurable neuromorphic hardware system that can readily be applied to real-world computing problems.The remarkable sensory and behavioral capabilities of all higher organisms are provided by the network of neurons in their nervous systems. The computing principles of the brain have inspired many powerful algorithms for data processing, most importantly the perceptron and, building on top of that, multilayer artificial neural networks, which are being applied with great success to various data analysis problems (1). Although these networks operate with continuous values, computation in biological neuronal networks relies on the exchange of action potentials, or “spikes.”Simulating networks of spiking neurons with software tools is computationally intensive, imposing limits to the duration of simulations and maximum network size. To overcome this limitation, several groups around the world have started to develop hardware realizations of spiking neuron models and neuronal networks (2–10) for studying the behavior of biological networks (11). The approach of the Spikey hardware system used in the present study is to enable high-throughput network simulations by speeding up computation by a factor of 10⁴ compared with biological real time (12, 13). It has been developed as a reconfigurable multineuron computing substrate supporting a wide range of network topologies (14).In addition to providing faster tools for neurosimulation, high-throughput spiking network computation in hardware offers the possibility of using spiking networks to solve real-world computational problems. The massive parallelism is a potential advantage over conventional computing when processing large amounts of data in parallel. However, conventional algorithms are often difficult to implement using spiking networks for which many neuromorphic hardware substrates are designed. Novel algorithms have to be designed that embrace the inherent parallelism of a brain-like computing architecture.A common problem in data analysis is classification of multivariate data. Many problems in artificial intelligence relate to classification in some way or the other, such as object recognition or decision making. It is the basis for data mining and, as such, has widespread applications in industry. We interact with classification systems in many aspects of daily life, for example in the form of Web shop recommendations, driver assistance systems, or when sending a letter with a handwritten address that is deciphered automatically in the post office.In this work, we present a neuromorphic network for supervised classification of multivariate data. We implemented the spiking network part on a neuromorphic hardware system. Using a range of datasets, we demonstrate how the classifier network supports nonlinear separation through encoding by virtual receptors, whereas lateral inhibition transforms the input data into a sparser encoding that is better suited for learning.     

基于自组网和物联网的医疗设备综合管理系统设计与实现

目的:针对传统医疗设备管理系统电子信息化方面的不足,设计一种新的基于自组网和物联网技术医疗设备综合管理系统,以提高医院对医疗设备的管理效率。方法:引入自组网和云计算网络技术,实现各个管理系统模块之间快速无缝链接,并提供完善的操作手册和设备状态等信息。结果:该系统采用了新型监控和无线组网技术,具有监控医院所有医疗仪器设备的运行状况,并记录和统计各种运行信息,实现对医院所有医疗仪器设备的管理、跟踪和统计等功能;此外,提供的手册可以方便地检查仪器设备的健康状态,以便于维护和维修。结论:基于自组网和物联网技术医疗设备综合管理系统,通过实际部署使用,验证了该系统的成熟性和可靠性,可以大幅提高医疗设备的使用效率。     

Increased NUCKS expression is a risk factor for poor prognosis and recurrence in endometrial cancer

Nuclear ubiquitous casein and cyclin-dependent kinases substrate (NUCKS) was reported to function as a potential biomarker in various tumors. Thus, we aimed to explore the expression of NUCKS in endometrial cancer (EC) and its clinical significance using quantitative real-time PCR (qRT-PCR) and immunohistochemistry (IHC). qRT-PCR results showed that NUCKS mRNA expression gradually elevated from normal endometrium to atypical endometrial hyperplasia, and to EC (P < 0.05 between each group). NUCKS overexpression was strongly associated with FIGO stage (P = 0.002), histologic grade (P = 0.029), lympho-vascular space involvement (P = 0.014), lymph node metastasis (P = 0.019), and recurrence (P < 0.001). Cox multivariate analysis revealed that NUCKS overexpression was an independent factor for overall survival and recurrence-free survival (P < 0.001 for both). Multivariate logistic regression suggested that recurrence was independently correlated with NUCKS overexpresion (P = 0.039), FIGO stage (P = 0.002), and lymph node metastasis (P = 0.002). In summary, NUCKS overexpression may function as a potential biomarker for prognosis especially for recurrence in ECs.     

国内云计算教育应用的研究现状与趋势分析

云计算作为一项新兴的信息技术，自从2008年引入我国后，受到国内教育者和研究者的普遍关注。文章从文献数量和应用主题两个角度对我国2008年-2012年间与云计算教育应用相关文献进行了归类统计，分析了当前国内云计算教育应用研究现状，同时对国内云计算教育应用的研究趋势进行了初步展望。     

基于无线网络架构的输液室移动信息管理系统设计与开发

重点阐述了输液室移动信息管理系统设计理念与技术要点,诠释了该系统将无线应用与条码技术、移动计算相捆绑,以PDA为移动终端载体,为医疗行为带来了高效率、严质控、深管理等一系列显著变化。     

大数据时代无服务器计算在科技情报服务中的应用

无服务器计算和科技情报的有机结合可形成数据、服务、应用的生态闭环，有助于为未来科技情报工作的发展提供有力支撑。研究分析了云计算在当前科技情报服务中的弊端和基于无服务器计算的科技情报服务平台的优势，总结了科技情报服务的基本流程和无服务器计算在科技情报服务系统中的应用经验，结合大数据、无服务器计算等技术和科技情报服务方法论构建了基于无服务器计算的科技情报服务系统模型和架构，为今后构建无服务器科技情报服务系统的相关研究与实践提供新思路。     

泛在学习环境下超声医学住培多元反馈教学模式的构建应用

摘要]:目的 对泛在学习环境下超声住院医师规范化培训（简称住培）构建多元反馈教学模式的效果进行评价。方法 选取2019年1月—2020年10月在内蒙古医科大学附属医院超声医学科规培轮转的70名住院医生作为研究对象，按随机数字表法分为对照组和实验组，每组35名，对照组根据专题训练法开展住培教学，期间结合信息化教学手段开展必要的在线教学；实验组带教者通过对大数据时代“互联网+”条件的深入分析与掌握，构造出基于泛在学习环境下的多元反馈教学模式。比较两组学员超声医学专科技术过程性考核成绩，使用教学评估问卷对两组学员进行“教学认可度调查”。结果 实验组学生超声医学专科技术过程性考核中的常见超声技术掌握程度、诊断报告书写、文献分析作业及阅片成绩等方面均明显高于对照组(均P<0.05)；在问卷调查中，实验组学员对泛在学习环境下的多元反馈教学模式普遍满意，各项指标满意度均在80%以上，多项教学指标的满意度优于对照组(P<0.05)。结论 泛在学习环境下的多元反馈教学模式适应“互联网+”大背景下的医学在线课程建设与发展，有利于提高超声住院医师规范化培训教学课